quick & dirty Olympus Vanta plotting example

notebooks/witzit-plot-vanta.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# witzit-plot-vanta --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
+    "#\n",
+    "# Copyright (C) 2019-2022, Jeff Moe\n",
+    "#\n",
+    "# This program is free software: you can redistribute it and/or modify\n",
+    "# it under the terms of the GNU General Public License as published by\n",
+    "# the Free Software Foundation, either version 3 of the License, or\n",
+    "# (at your option) any later version.\n",
+    "#\n",
+    "# This program is distributed in the hope that it will be useful,\n",
+    "# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
+    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
+    "# GNU General Public License for more details.\n",
+    "#\n",
+    "# You should have received a copy of the GNU General Public License\n",
+    "# along with this program.  If not, see <http://www.gnu.org/licenses/>.\n",
+    "#\n",
+    "# Plotting Application.\n",
+    "# https://spacecruft.org/spacecruft/witzit\n",
+    "#\n",
+    "# Based on wut?\n",
+    "# https://spacecruft.org/spacecruft/satnogs-wut"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import locale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "locale.setlocale(locale.LC_ALL, \"en_US.utf8\")\n",
+    "plt.rcParams['axes.formatter.use_locale'] = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('examples/olympus-vanta.csv', header = 0,\n",
+    "                 skiprows=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40], usecols = [0, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_theme()\n",
+    "plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n",
+    "# XXX y=\n",
+    "g=sns.lineplot(x=\"Date\", y=\"2022-04-08\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange')\n",
+    "g.set_title('XRF Spectrum'.format('seaborn'), color='xkcd:cornflower blue', fontsize='large', fontweight='bold')\n",
+    "g.set_xlabel('energy (keV)', color='xkcd:goldenrod', fontsize='large', fontweight='bold')\n",
+    "g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n",
+    "g.patch.set_alpha(1.0)\n",
+    "g.set_facecolor('xkcd:powder blue')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"xrf-spectrum.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}